Glacier Retreat Has Been

SlowingDuring 2000-2016

A new satellite-based estimate of glacier mass change for High Mountain Asia (HMA), the world’s third largest glacier conglomeration after Antarctica and Greenland, reveals a relatively modest retreat rate for the first 17 years of the 21st century.

The results from Brun et al. (2017) indicate that HMA glaciers are nearly in balance for 2000-2016, contributing the equivalent of just 0.046 millimeters per year to sea level rise during this period. This is a glacier-melt contribution rate of less than half a centimeter per century, which is “much smaller” and “in marked disagreement” with other recent estimations.

In this study, for example, glaciers in the Kunlun and Karakoram regions of HMA have been observed to be in balance or even gaining mass. The model-based studies indicate significant mass losses are occurring in these regions.

The high estimates of HMA glacier melt and sea level rise contribution are preferred (and thus “widely used in the literature”) by those advancing the position that modern glacier melt rates are unusual or unprecedented. This study makes a strong case that past and present high glacier-melt values in this region are unsupported by comprehensive analyses from satellite observations.

‘Much Smaller’ Asian Glacier RetreatFor 2000-2016

•“We provide spatially resolved estimates for the potential contribution of HMA [High Mountain Asia] glaciers to SLR [sea level rise] and changes in the downstream hydrology, aggregated by major river basins. We find a total sea level contribution of -16.3 ± 3.5 Gt yr−1 (14.6 ± 3.1 Gt yr−1 when including only the exorheic basins), corresponding to0.046 ± 0.009 mm yr−1 sea level equivalent (SLE)(0.041 ± 0.009 mm yr−1 SLE when including only the exorheic basins).” [-16.3 Gt yr is equivalent to +0.046 mm yr, or adding only 0.46 of a centimeter to sea levels in 100 years.]

•“This estimate is in marked disagreement with the total estimate of −46 ± 15 Gt yr−1 fromCogley, 2009 and Marzeion et al., 2015commonly used in the sea level budget studies.”

Model-Based Estimates Of Glacier Retreat ‘Four Times Larger’ Due To ‘Lack Of Direct Measurements’

•“The model contribution estimates ofCogley, 2009 and Marzeion et al., 2015for the period 2000–2013 are nearly four times larger than our estimate for Central Asia (22 Gt yr−1 for the model versus 6 Gt yr−1 for this study) and over twice as large for South Asia East and South Asia West (14 Gt yr−1 for the model versus 6 Gt yr−1 for this study, and 9 Gt yr−1 for the model versus 4 Gt yr−1 for this study for the two regions respectively.”

•“These discrepancies can be explained by the lack of direct measurements to constrain both the interpolation method of Cogley, 2009 and the model tuning and/or the high temporal smoothness of atmospheric models of Marzeion et al., 2015.”

•“In particular, these estimates [Cogley, 2009 and Marzeion et al., 2015] attribute mass losses to Karakoram and Kunlun, two regions with a large glacierized area where we find only little mass loss or even mass gain.”

Shrinking Estimates Of Non-Polar Glacier Mass Loss

In a highly-regarded mass balance analysis (to date, nearly 550 citations) published in the journal Nature, Jacob et al. (2012) record a stark trend reversal and rapid deceleration of glacier retreat in the 21st century for the globe’s glaciers and ice caps (excluding the Greenland and Antarctica ice sheets).

A major reason why the glacier loss estimates have been shrinking in recent years is the effectively in-balance estimates for HMA glaciers, which “showa mass loss of only 4 ± 20 Gt yr−1 for 2003–2010, compared with 47–55 Gt yr−1 in previously published estimates“.

Considering aggregate estimates for non-polar glacier mass loss were 2 or 3 times greater for the 1990s to early 2000s than in the more recent years, it would appear the trend reversal in glacier mass losses is more widespread than a few areas in High Mountain Asia.

“Here we show that GICs [glaciers and ice caps], excluding the Greenland and Antarctic peripheral GICs, lost mass at a rate of 148 ± 30 Gt yr−1 from January 2003 to December 2010, contributing 0.41 ± 0.08 mm yr−1 to sea level rise.”

“The high mountains of Asia, in particular, show a mass loss of only 4 ± 20 Gt yr−1 for 2003–2010, compared with 47–55 Gt yr−1 in previously published estimates (Matsuo and Heki, 2010, Dyurgerov, 2010).”

If I understand correctly, their method was to use stereo satellite images to measure the (almost) entire ice mass in 3D. Sounds sensible.

I’ve not paid to download the whole paper but there is a reference in the snippet above to ‘bridging the gap between two previous methods’, one of which sounds as unsophisticated as taking spot measurements then doing some (probably rather dodgy) averaging. Perhaps the other is simple 2d satellite images…?

Can anyone tell me what these conventional methods are and whether they are even credible in principle (i.e. before being…ahem…corrected)?

“We apply a fully automated method to compute DEMs from the vast amount of freely available ASTER optical satellite stereo pairs. We use these DEMs to assess glacier volume changes over the entire HMA for the period 2000–2016. We fit a linear regression through time series of co-registered ASTER DEMs to estimate the rate of elevation change for each 30-m pixel. Inspired by previous studies, this methodology was further developed and validated on the Mont Blanc area in the European Alps21. Contrary to earlier studies, we did not rely on DEMs available online (the so-called 14DMO product) but directly calculated more than 50,000 DEMs from L1A ASTER images using the Ames Stereo Pipeline. One strength of this method is that it relies exclusively on satellite optical data. Thus, it is not affected by signal penetration, which is a major source of uncertainty in DEMs derived from radar sensors (for example, from the Shuttle Radar Topography Mission; SRTM), for which the signal penetrates to a mostly unknown depth of up to many metres into snow and ice.”

“We integrate these elevation changes and use a mass to volume conversion factor of 850 ± 60 kg m−3 (ref. 27 and Supplementary Information). Our glacier mask for DEM co-registration and for integrating the glacier elevation change is derived from the GAMDAM inventory, as this is the only homogeneous inventory covering the entire HMA. As a sensitivity test, we compare our GAMDAM-based estimates with those obtained using the ICIMOD inventory, the ESA CCI inventory and the Randolph Glacier Inventory (Supplementary Information).”